La cultura informática en la enseñanza-aprendizaje del inglés
This study presents a model designed to predict academic performance using neural networks. It is framed within a quantitative approach and is categorized as a multivariate correlational study. The research is based on a database from an educational institution, available in the data repository of t...
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| Format: | Book |
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Universidad Latinoamericana,
2024-09-01T00:00:00Z.
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| Summary: | This study presents a model designed to predict academic performance using neural networks. It is framed within a quantitative approach and is categorized as a multivariate correlational study. The research is based on a database from an educational institution, available in the data repository of the University of California, Irvine. R was chosen as the programming language, with RStudio as the development environment. The CRISP-DM methodology was adopted to carry out the data analysis. The construction of the neural network was carried out using the nnet package, available in the Comprehensive R Archive Network (CRAN). The neural network model was applied to data collected from 649 students, and its predictive ability was comprehensively evaluated. After comparing it with a multiple linear regression model, it was observed that the neural network model achieved an effectiveness of 87% in predicting academic performance, evidencing its suitability for this purpose. |
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| Item Description: | 3006-1385 10.62319/simonrodriguez.v.4i8.32 |